In [1]:
import pandas as pd
import seaborn as sns
import plotly.express as px
import numpy as np

import matplotlib.pyplot as plt
In [2]:
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib¶

For this excercise, we have written the following code to load the stock dataset built into plotly express.

In [3]:
stocks = px.data.stocks()
stocks.head()
Out[3]:
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708

Question 1:¶

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

In [4]:
info = stocks['GOOG']
date = stocks['date']

fig, ax = plt.subplots(figsize=(12,9))
ax.plot(date, info)
ax.set_xticks(np.arange(0, len(date)+1, 14)) #length of 105, so to show 8, steps of 14 needs to be taken
ax.set_title('Google stock')
ax.set_ylabel('stock value')
ax.set_xlabel('date')
plt.show()

Question 2:¶

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

In [5]:
stocks.plot(x = 'date', xticks = (np.arange(0, len(stocks['date'])+1, 14)), 
            figsize = (12,9), title = 'Stocks', ylabel = 'stock value')
Out[5]:
<AxesSubplot:title={'center':'Stocks'}, xlabel='date', ylabel='stock value'>

Seaborn¶

First, load the tips dataset

In [6]:
tips = sns.load_dataset('tips')
tips.head()
Out[6]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Question 3:¶

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

  • Are there differences between male and female when it comes to giving tips?
  • What attribute correlate the most with tip?
In [7]:
#Question 1
print('Are there differences between male and female when it comes to giving tips?')
sns.scatterplot(x = 'total_bill', y = 'tip', data = tips, hue = 'sex');

Are there differences between male and female when it comes to giving tips?
In [8]:
# fig = plt.figure(figsize=(28,9))


# # g1 = sns.FacetGrid(tips, row = 'total_bill', col='tip', hue='sex')
# # g1.map(sns.scatterplot, 'total_bill', 'tip')
# # g1.add_legend()

# g1 = sns.FacetGrid(tips, col='size', hue='sex')
# g1.map(sns.scatterplot, 'total_bill', 'tip')
# g1.add_legend()

# g1 = sns.FacetGrid(tips, col='time', hue='sex')
# g1.map(sns.scatterplot, 'total_bill', 'tip')
# g1.add_legend()

#fig1 = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")

Plotly Express¶

Question 4:¶

Redo the above exercises (questions 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset¶

Hints:

  • Turn stocks dataframe into a structure that can be picked up easily with plotly express
In [9]:
# print(stocks.columns)
# list1 = stocks['GOOG']
fig = px.line(stocks, x = 'date', y = stocks.columns, title = 'Stocks', markers = True)
fig.update_traces(mode = 'lines+markers')
fig.show()


#Idon't know how to get different symbols at each line

The tips dataset¶

In [10]:
fig1 = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")
fig1.show(figsize = (12,9))

Question 5:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [11]:
#load data
df = px.data.gapminder()
df.head()
Out[11]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4
In [12]:
# YOUR CODE HERE

df2007 = df.query('year == 2007')
new = df2007.groupby('continent').sum()

fig = px.bar(new, x = 'pop', y = new.index, color = new.index,
             orientation = 'h', text_auto = '.2s')
fig.update_yaxes(categoryorder = 'total ascending')
fig.update_traces(textposition="outside", showlegend = False)
fig.show()
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